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. 2026 Feb 12;14:1740303. doi: 10.3389/fpubh.2026.1740303

Temporal trends of Shigella outbreaks in the United States, 2009–2022

Muhammad Rashid Bajwa 1,†, Charles Ayooluwa Adegbole 1,†, Rebecca Lee Smith 1,*,†
PMCID: PMC12936016  PMID: 41769129

Abstract

Background

Shigella infections remain a significant public health threat in the United States because of their diverse outbreak patterns and high disease impact, particularly among young children. This study aimed to investigate the trends in Shigella outbreak incidence and demographic patterns by age and sex from 2009 to 2022 in order to inform targeted prevention strategies.

Methods

We performed a retrospective observational analysis of 1,208 Shigella outbreak surveillance data sourced from the Centers for Disease Control and Prevention (CDC) via the National Outbreak Reporting System (NORS). The incidence rates per 1 million population–years were calculated overall and by age and sex. Descriptive statistics were used to summarize the data, and joinpoint regression with 95% confidence intervals was used to identify trends.

Results

The outbreak incidence rate was 0.27 (95% CI: 0.01–0.84) per 1 million population–years in 2009, peaking at 0.74 (95% CI: 0.65–0.84) in 2016 and declining to 0.03 (95% CI: 0.01–0.05) by 2022. Children aged 1–4 years had the highest incidence, and females had a slightly higher incidence rate than males, as indicated in outbreak–associated case data. Joinpoint incidence trends showed an annual percent change (APC) of 25.12% (95% CI: 11.47–53.76, p = 0.001) from 2009 to 2015, followed by a decline with an APC of −38.91% (95% CI: −60.07 to −29.57, p < 0.001). The outbreaks were predominantly person–to–person transmission, with the highest frequency occurring in childcare settings.

Conclusion

These findings underscore the need for targeted prevention strategies, such as ongoing hygiene education and sanitation enhancements in childcare, as well as reminders about Shigella prevention in shared educational facilities before the fall semester.

Keywords: disease outbreaks, joinpoint regression, NORS, retrospective studies, Shigella

1. Introduction

Shigellosis, caused by Shigella spp., is among the leading causes of diarrheal infections worldwide, with an estimated 80–165 million cases and 600,000 deaths annually (1). In the United States (US), Shigella causes an estimated 500,000 cases, 100,000 hospitalizations, and 500 deaths each year (2). Shigella spp. are highly infectious (3), and are transmitted primarily through the fecal–oral route, including person–to–person contact, and via contaminated food, water, and the environment (4). The antimicrobial resistance of Shigella to third–generation cephalosporins is increasing, with a more than doubling from 5% in 2020 to 12.3% in 2022, alongside recent findings of high resistance to azithromycin (42%) and ciprofloxacin (18%) (5). The high incidence and development of multidrug resistance make Shigella spp. a critical public health and sustainability concern in the food industry.

Shigellosis was the third most common cause of enteric outbreaks in the US (after Norovirus and Salmonella) (6) and the second most common outbreak etiology among school and childcare settings (after Norovirus) from 2009 to 2019 (7). Although outbreaks among unhoused individuals and men who have sex with men (MSM) have also been reported (2, 8), children aged 1–4 years consistently exhibited the highest incidence rates during 2009–2018 in the US (9). This age concentration underscores the need to better understand transmission dynamics in community and childcare environments.

Inadequate sanitation, overcrowding, and poor hygiene are significant modifiable risk factors that disproportionately contribute to Shigella transmission in socioeconomically disadvantaged communities (10). Environmental factors, particularly seasonal temperature fluctuations, have also been shown to influence Shigella outbreak patterns, with higher incidence rates typically observed during warmer periods (8, 11, 12). These contextual factors underscore the importance of assessing long-term trends and their underlying drivers to inform targeted prevention efforts.

Previous studies have focused on the 10 US states within the Foodborne Diseases Active Surveillance Network (FoodNet), describing incidence rates, demographics (9, 13–17), and enteric outbreaks (2009–2018) (6); waterborne disease outbreaks associated with drinking water (18); untreated recreational water outbreaks from three US states (2008–2019) (19), acute gastroenteritis outbreaks by transmission mode (2009–2013) (20), and acute gastroenteritis outbreaks by person–to–person contact (2009–2010) (21). However, these previous studies have not fully addressed the long–term patterns underlying Shigella outbreaks. Despite the importance of Shigella outbreaks to public health in the US, comprehensive, long-term (14-year) studies describing the epidemiology, demographics, outbreak rates, outbreak-associated incidence, and temporal trends in the US as a whole are lacking.

Our study aimed to (1) assess the epidemiological characteristics of Shigella outbreaks between 2009 and 2022, including demographics, outbreak settings, and transmission modes, and (2) identify and quantify changes in Shigella outbreaks and outbreak–associated case incidence rates (IRs) during the study period.

Our study employed joinpoint regression analysis, a widely used statistical method for detecting and characterizing significant changes in temporal trends across various health indicators (22). Applying joinpoint regression to analyze national Shigella outbreak surveillance data from the National Outbreak Reporting System (NORS) represents a novel approach for examining trends in outbreak rates and associated case incidences.

2. Methods

2.1. Data sources and study design

We obtained Shigella spp. outbreak data from the Centers for Disease Control and Prevention’s (CDC) National Outbreak Reporting System (NORS) via a data request. The dataset included Shigella outbreaks with first illness onset dates from January 1, 2009, to December 31, 2022. NORS is a web-based central repository for outbreak data compiled from investigations and voluntary reports by territorial, local, and state health departments in the US Since 2009, this database has been the standard for all enteric disease outbreaks, including those caused by Shigella spp. in the US (23). An outbreak is defined as two or more cases of similar illness with a shared exposure (23). Outbreaks with at least two primary illnesses with confirmed or suspected Shigella etiology were selected.

The epidemiological characteristics of each outbreak, including geographic location, mode of transmission, date of onset of the first illness, and incubation period, were extracted from the NORS database. Moreover, the dataset included aggregated data on cases of illness within the outbreaks, including outcomes (hospitalizations, emergency room visits, and deaths), sex (male, female, and unknown), case status (confirmed, probable, and estimated total), and age group (<1, 1–4, 5–9, 10–19, 20–49, 50–74, and ≥75 years); these data were provided in a relational format. In NORS, outbreak-associated cases are classified as confirmed, probable, and estimated. For incidence calculations, we used the CDC-estimated case counts, which incorporate all reported case classifications. We selected outbreaks with at least two primary illnesses with confirmed or suspected Shigella etiology, consistent with the standard NORS definition that had already been applied in the finalized CDC dataset. This approach allowed us to include all outbreaks, including those with small case counts or from states with only a single reported outbreak. We used annual population data from 48 contiguous states and the District of Columbia (DC) from the US Census Bureau (24) for the statistical analysis.

Exposure-state assignments followed the CDC-defined Exposure State variable in the NORS dataset, with no recoding or modification by the authors. For outbreaks with a single exposure state, the Exposure State field identifies where exposure occurred, even when cases reside in multiple states; the corresponding Exposure State Count field provides the number of cases associated with that state. For outbreaks involving multiple exposure states, both fields remain blank, and the separate States table documents all implicated states, whether exposure or residency states. We used the exposure-state assignments exactly as provided by CDC in the finalized dataset of 1,226 outbreaks reported from 2009 to 2022.

2.2. Statistical analysis

2.2.1. Descriptive analysis

We calculated the annual Shigella outbreak incidence rates per 1 million population–years, overall and by transmission mode, from 2009 to 2022, by dividing the total number of outbreaks each year by that year’s population and multiplying by 1 million. We also calculated outbreak–associated case-wise incidence rates per 1 million population–years overall and by sex and age group. The 95% confidence intervals (CIs) for incidence rates were calculated via the Poisson distribution. For the age- and sex-stratified incidence analyses, we included only outbreak-associated cases with known age or sex information. Cases with missing or unspecified age or sex were excluded solely from the corresponding stratified calculations; however, the outbreaks to which these cases belonged remained in the overall dataset. We also summarized the epidemiological characteristics of the outbreaks and associated cases, including reported Shigella species, symptoms, exposure settings, and transmission modes, by year and by month to investigate temporal and seasonal trends in outbreak characteristics.

We performed all the data cleaning, statistical analysis, summaries, and visualizations using the R environment version 4.5.0 (25) within RStudio IDE version 2025.5.1.513 (26).

2.2.2. Joinpoint regression analysis

Our study employed Joinpoint Regression Analysis to analyze trends in Shigella outbreak data from 2009 to 2022, using annual aggregations of four population-standardized datasets: outbreak counts, outbreak-associated case counts, sex-specific case counts, and age-specific case counts, with rates standardized per 1 million population years. We used the Joinpoint Regression Program (version 5.4.0) (27) to identify change points in trends, with a significance level of α < 0.05, and assumed Poisson variance to calculate the Annual Percent Change (APC). Given our 14 annual data points (2009–2022), we allowed a maximum of two joinpoints via the Gris Search method, which is consistent with the software’s recommendations for model stability (22). The Weighted Bayesian Information Criterion (WBIC) (28) was used to determine the optimal number of joinpoints by balancing model fit and complexity. The empirical quantile method was used to calculate 95% confidence intervals for the Annual Percent Change (APC) and joinpoint locations. The Average Annual Percent Change (AAPC) was estimated to summarize the overall trend across the study period (2009–2022) (22).

3. Results

3.1. Outbreak incidence

We extracted data from 1,226 Shigella outbreaks reported to NORS with a first illness onset between 2009 and 2022. For this and future state-level spatial analyses, we included only outbreaks from the 48 contiguous states and the District of Columbia (DC), resulting in 1,208 outbreaks after excluding Alaska and Hawaii to maintain geographic contiguity. The incidence of Shigella outbreaks in the United States decreased from 0.27 (95% CI: 0.21–0.33) in 2009 to 0.03 (95% CI: 0.01–0.05) in 2022, with a peak of 0.74 (95% CI: 0.65–0.84) per million population–years in 2016. After stabilizing at approximately 0.22–0.29 post–2016, outbreak rates reached their lowest level during the COVID–19 pandemic years (0.09 in 2020 and 0.04 in 2021), remaining at 0.03 per million population–years in 2022.

In the study population, person–to–person transmission was the most common primary mode, with 0.49–0.64 outbreaks per million population years in the high–activity years of 2015–2016 and 0.13–0.25 outbreaks per million population years in the lower–activity years. The only exception to this was for 2020–2022, during which lower person–to–person transmission corresponded to the first two years of the COVID–19 pandemic (Figure 1; Supplementary Table S6).

Figure 1.

Line graph showing incidence rates per one million population for various disease transmission modes from 2009 to 2021. Total and person-to-person modes show sharp peaks around 2015, then decline, while other modes remain low.

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Shigella outbreak incidence rate per 1 million population by transmission mode in the United States, 2009–2022.

3.2. Outbreak–associated case incidence

The Shigella outbreak–associated case incidence in the United States followed a similar pattern to the outbreak incidence, declining from 4.03 (95% CI: 3.80–4.26) in 2009 to 0.24 (95% CI: 0.19–0.29) in 2022, with a peak of 13.34 (95% CI: 12.88–13.68) per million population–years in 2015. After maintaining rates of approximately 2.37–3.42 from 2016 onward, incidence rates dropped to their lowest levels during the COVID–19 pandemic years (0.96 in 2020 and 0.20 in 2021), before stabilizing at 0.24 per million population–years in 2022. Within outbreaks that included cases from both females and males, females consistently had higher incidence rates than males (Figure 2a; Supplementary Table S7).

Figure 2.

Two line charts show the incidence rate per one million population from 2009 to 2021. Panel (a) compares incidence rates by sex—total, male, and female—with all groups peaking around 2015 and declining sharply afterwards. Panel (b) breaks down incidence rates by age groups, with the highest rates among children under five years old, especially ages one to four, peaking in 2015 and then decreasing substantially across all groups.

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Shigella outbreak–associated case incidence rate per 1 million population in the United States, 2009–2022: (a) Incidence rate by sex; (b) incidence rate by age group.

Among the outbreaks involving multiple age groups, the highest incidence was consistently observed in children aged < 10 years, especially in the 1–4-year age group (Figure 2b; Supplementary Table S7). Within each age group, females had a higher incidence rate than males, except for those aged 75 years and older, where the difference was minimal. Person–to-person transmission predominated throughout the study period, accounting for 52.1 to 89.1% of the outbreaks between 2009 and 2019, peaking at 89.1% in 2017 and declining to 52.1% by 2019. The person–to-person transmission rate decreased to 32.1% in 2020 and 23.1% in 2021 during the COVID-19 pandemic, before increasing to 44.4% in 2022. Indeterminate/unknown modes were the second most common mode, ranging from 6.2 to 46.4%, with a peak of 46.4% in 2020. Foodborne transmission contributed 1.3 to 33.3%, showing variability but no clear trend, whereas waterborne and environmental modes remained minor, accounting for less than 11.1% of outbreaks annually (Figure 3a; Supplementary Table S9). Childcare/preschool was the leading exposure setting category, accounting for 28.3 to 61.3% in the pre-2020 years and declining to 14.3 to 22.2% during the 2020–2022 period. Schools/colleges/universities ranked second, with rates ranging from 7.5 to 26.8%; ‘other’ settings ranked third, with rates ranging from 5.4 to 32.1%; and unknown settings ranked fourth, with rates ranging from 11.3 to 47.2%. Retirement communities, healthcare facilities/hospitals, religious institutions, restaurants, and community-wide events collectively accounted for less than 6.2% of the total (Figure 3b; Supplementary Table S10).

Figure 3.

Two stacked bar charts display outbreak proportions by year from 2009 to 2022. Chart (a) categorizes outbreaks by transmission mode with colors for person-to-person, food, water, environmental, animal contact, and indeterminate or unknown modes. Chart (b) groups outbreaks by setting exposure, including childcare, schools, restaurants, healthcare, retirement communities, community-wide, religious places, other, and unknown, each with a distinct color in the legend. Numbers above bars indicate total outbreaks per year. Both charts show a notable increase in indeterminate/unknown and unknown categories after 2019.

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Reported Shigella outbreaks in the United States, 2009–2022: (a) Transmission mode; (b) exposure setting.

We identified clear seasonal patterns in Shigella outbreaks, with the highest outbreak counts occurring in July (119 outbreaks, 9.9%), September (121, 10.0%), and October (124, 10.3%), and the lowest in November (70, 5.8%) and December (74, 6.1%) from 2009 to 2022 (Supplementary Table S11). We identified seasonal joinpoint trends in Shigella outbreak frequency, revealing an increasing trend until October, with an APC of 3.03% (95% CI: 0.76 to 7.60, p = 0.013), and a decreasing trend thereafter, with an APC of −25.79% (95% CI: −37.31 to −9.21, p < 0.05) (Supplementary Figure S4). We found no clear seasonal pattern in transmission modes, with person-to-person transmission being the dominant mode across all months. We identified a significant portion of the seasonality to exposure settings, with childcare and preschool settings being implicated in 38.7 to 57.1% of the monthly outbreaks between March and August. These outbreaks peaked in April (57.1%) and June (56.8%). In contrast, we observed that educational institutions, including schools, colleges, and universities, accounted for 34.7% of the outbreaks in September and 30.0% in November. However, their contribution was less than 2% in June and July. The other settings accounted for 10.6 to 21.5% of the monthly outbreaks, whereas the unknown settings ranged from 10.9 to 31.9% of the monthly outbreaks. Settings such as retirement communities, healthcare facilities, religious places, restaurants, and community-wide events collectively accounted for an average of 4.3%.

3.3. Joinpoint regression trends in outbreak incidence

Figure 4 presents the aggregate joinpoint regression analysis of Shigella outbreak rates per million population years, categorized by the primary mode of transmission. The overall model detected a single joinpoint in 2016, revealing a significant increase from 2009 to 2016, with an APC of 21.78% (95% CI: 11.98–42.0, p < 0.0002), followed by a substantial decrease from 2016 to 2022, with an APC of −36.2% (95% CI: −58.97 to −25.27, p < 0.001). The AAPC was −9.64% (95% CI: −19.6 to −2.7, p = 0.015).

Figure 4.

Line graph displaying incidence rates per one million population from 2009 to 2022 for various transmission modes using joinpoint models. All modes and person-to-person transmission peaked in 2016 before declining sharply. Food, water, environmental, and indeterminate/unknown transmission modes show lower rates with generally modest increases until around 2019, followed by decreases. The legend details average percent changes for each mode and period.

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Joinpoint trends in Shigella outbreak incidence rates by transmission mode in the United States, 2009–2022.

Person–to–person transmission displayed a parallel joinpoint in 2016, with an APC of 22.8% (95% CI: 12.8–42.46, p < 0.001) in the first segment and −45.24% (95% CI: −69.4 to −33.96, p < 0.001) in the second segment, resulting in an AAPC of −15.4% (95% CI: −28.7 to −7.9, p < 0.001). The percentage of foodborne transmission APCs decreased from 28.35% between 2009 and 2014 to 17.28% between 2014 and 2022. Shigella sonnei exhibited the same trends as those associated with person–to–person transmission, and other Shigella species did not show any significant trends (Figure 4; Supplementary Figure S3; Supplementary Table S1).

3.4. Joinpoint regression trends in outbreak–associated case incidence

The joinpoint model demonstrated a significant (p < 0.05) decrease in the incidence rates, with an AAPC of −14.95% (95% CI: −28.21, −7.63) over the entire study period (2009–2022) (Figure 4). We observed two significant trends in incidence rates over time: an increase from 2009 to 2015 (APC = 22.73%; 95% CI: 10.65, 46.12) and a decrease from 2015 to 2022 (APC = −35.65%; 95% CI: −53.90, −27.10) (Supplementary Figure S2; Supplementary Table S3).

Between 2009 and 2016, a significant increase in incidence was notable among children aged <1 year (36.6%), those aged 1–4 years (24.9%), those aged 5–9 years (14.2%), and adults aged 20–49 years (22.8%). Between 2016 and 2022, a decrease was observed in the same age groups: <1 year (APC: −48.2%), 1–4 years (APC: −49.1%), 5–9 years (APC: −52.8%), and 20–49 years (APC: −33.5%). Interestingly, the incidence among adolescents aged 10–19 years increased from 2009 to 2011 (APC: 99.6%), then decreased steadily until 2022 (APC: −19.64%), with one joinpoint detected. The AAPC during the observed study period (2009–2022) significantly decreased only among those aged <50 years. Moreover, the incidence rate in the 50 years and above age group did not significantly differ between 2009 and 2022 (Figure 5; Supplementary Table S3).

Figure 5.

Line graph comparing incidence rates per one million population across age groups and years, 2008 to 2022, using joinpoint regression models. Distinct colored lines and markers represent different age groups, each with varying trends and annual percent changes noted in the legend. Incidence rates peaked sharply in 2016 for younger groups, especially ages one to four, then declined rapidly, while rates for older groups remained low and more stable. Data visualization illustrates varying age-dependent trends and significant annual percent changes, highlighting shifts before and after 2016.

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Joinpoint trends in Shigella outbreak–associated case incidence rates among age groups in the United States, 2009–2022.

With respect to sex, both sexes had joinpoints in 2015, with APCs before and after 2015, and the overall AAPC resembled the overall trends. Although Shigella outbreak-associated case incidence rates were higher among females than among males, females showed a more negative AAPC (−14.67% compared with −13.35%) (Supplementary Figure S2; Supplementary Table S3).

4. Discussion

This study analyzed Shigella outbreak data from the National Outbreak Reporting System (NORS) to calculate outbreak rates and outbreak-associated case incidence rates per 1 million population-years in the United States from 2009 to 2022. We identified annual proportions and seasonal patterns in exposure and transmission modes across outbreak settings. In addition, joinpoint regression analysis characterized temporal trends in these rates and identified a significant shift in the incidence trajectory between 2015 and 2016.

Shigella outbreak incidence rates per 1 million population-years peaked in 2016 (0.74, 95% CI: 0.65–0.84) and reached their lowest point in 2022 (0.03, 95% CI: 0.01–0.05), with person-to-person transmission consistently dominating outbreaks, while waterborne and environmental transmission remained negligible. Shigella outbreak-associated case incidence rates peaked in 2015 and similarly declined to their lowest rates in 2022. The age-specific Shigella incidence rate was highest among children <10 years, mirroring the overall outbreak rate and its trajectory. The 1–4-year age group, in particular, had the highest rate throughout the study, consistent with the Shigella burden documented in previous studies (9, 29–32).

Shigella outbreak and case rates peaked around 2015–2016, likely reflecting the combined effects of enhanced detection and genuine changes in transmission. Several factors contributed to this rise, including an increase in outbreaks in childcare and school settings (7) and the widespread adoption of culture-independent diagnostic tests (CIDTs), which markedly increased surveillance sensitivity and identified infections previously missed by culture-based methods (13, 14, 33). Global molecular studies indicate that culture-based testing substantially underestimates the Shigella burden, underscoring the impact of diagnostic practices on reported incidence, particularly among young children (31, 34). During the same period, increases in multidrug-resistant Shigella (5, 35, 36) and outbreaks among unhoused persons (2, 8) and men who have sex with men (MSM) (2) may have further intensified transmission. This pattern aligns with a study from the United Kingdom indicating that extensively drug-resistant S. sonnei and continued MSM-associated transmission contributed to epidemic growth (37). However, we observed increases across all age groups, with the rise most pronounced among children under 10 years of age. This pattern suggests that intensified transmission within childcare and school settings, driven by both natural increases in spread and the heightened sensitivity of newer diagnostic methods, played a central role in the mid-2010s peak.

The post-2016 decline in Shigella outbreaks and incidence rates likely reflects a combination of improved prevention practices and broader reductions in reported enteric outbreaks nationwide (6, 7). Enhanced hygiene and sanitation measures in childcare facilities may have contributed to the initial pre-pandemic decrease, while contemporaneous declines in outbreak reporting may also have influenced national trends. The pronounced reduction observed during 2020–2022 aligns with the effects of COVID-19 mitigation measures, including social distancing, enhanced hygiene, and childcare and school closures, all of which suppressed fecal–oral pathogen transmission (17, 38). The sharp decrease in foodborne and person-to-person enteric infections during the pandemic period may additionally reflect changes in health-seeking behavior, reduced dining out, disruptions in diagnostic testing, and diminished capacity for outbreak surveillance and reporting (39, 40). Accordingly, trends observed during the pandemic years should be interpreted with caution, as this exceptional period may not fully represent outbreak dynamics under usual surveillance and transmission conditions.

Underlying medical conditions and concurrent infections may further shape susceptibility to shigellosis and affect clinical severity, particularly among immunocompromised or otherwise medically vulnerable populations (41–43). Evidence specific to Shigella spp. indicates that invasive outcomes such as bacteremia are uncommon but occur more frequently in higher-risk groups, including persons with HIV and individuals with comorbidities such as diabetes, cirrhosis, malignancy, and other immunosuppressive conditions (44, 45). HIV infection, in particular, has been identified as a factor that may increase both acquisition and transmission risk through prolonged carriage or altered immune responses (46). Long COVID has been increasingly linked to persistent immune dysfunction, chronic inflammation, and autoimmune mechanisms, which may potentially alter the risk of subsequent infections, including enteric bacterial diseases such as shigellosis (47, 48). Epidemiologic cohort studies have similarly assessed the post-acute burden of infections and other health outcomes following COVID-19, underscoring the broader infectious disease implications of pandemic-era immune perturbations (41). However, NORS does not capture prior SARS-CoV-2 infection history or long COVID status; therefore, these mechanisms cannot be directly assessed within the present outbreak-based analysis.

Although NORS does not systematically collect patient-level data on comorbidities or co-infections, co-circulating enteric pathogens and microbial interactions may also contribute to variation in susceptibility and clinical presentation. Prior studies have reported associations between viral pathogens, such as rotavirus and Shigella spp., suggesting that viral–bacterial interactions may influence diarrheal disease dynamics (49). Taken together, these host- and pathogen-related factors may contribute to differences in outbreak impact and warrant further investigation using linked clinical and laboratory surveillance data.

Our analysis of Shigella outbreaks revealed a clear seasonal trend, with 9.3–10.3% of annual outbreaks occurring from May to October, peaking in summer and fall due to increased social interactions in childcare and school/college/university settings, consistent with prior studies (6, 12, 50, 51). Childcare and preschool settings reported outbreaks year-round, whereas school, college, and university settings showed higher numbers in the fall months and markedly lower numbers in the summer, reflecting the dynamics of the academic calendar and periods of intensified contact among susceptible populations.

These findings are subject to several limitations. First, reporting Shigella outbreaks to NORS is voluntary, and not all variables are required, which may result in underreporting and incomplete information for key fields (e.g., age and sex), potentially underestimating their contribution to outbreak patterns. The adoption of CIDTs during the 2015–2016 period likely increased detection sensitivity, potentially biasing comparisons of trends before and after this transition. In later years, as CIDT use became more standardized, detection rates likely remained higher than pre-2015 levels, potentially making earlier years appear artificially lower. Although the post-peak decline aligns with broader reductions in enteric infections, these diagnostic shifts complicate the interpretation of long-term trends. NORS data are also affected by differences in surveillance methods, diagnostic capacity, and investigative resources across states. Additionally, NORS captures only outbreak-associated cases and omits sporadic shigellosis, which accounts for the majority of US infections and may follow different epidemiologic patterns, particularly among populations such as unhoused individuals or specific sexual networks, who may be underrepresented in outbreak investigations. Future work could integrate NORS data with other surveillance systems, including laboratory-based case reporting and antimicrobial resistance monitoring, to more fully characterize national transmission patterns and the contribution of emerging drug-resistant lineages.

These findings extend prior US descriptions of shigellosis by quantifying temporal changes and linking them to demographic and environmental factors (6, 7, 52). The dominance of person–to–person transmission and childcare–associated outbreaks underscores the need for targeted interventions. Behavioral and institutional factors strongly influence the success of these interventions, as sustained improvements in hand hygiene and environmental cleaning depend on consistent reinforcement, training, and administrative support. Evidence from broader public health research shows that preventive behaviors are most effective when individuals are supported by clear policies, adequate resources, and routine educational reminders (53, 54). Based on these findings, we recommend implementing year-round Shigella prevention education in childcare facilities, focusing on handwashing protocols and staff training, as well as issuing reminders to schools and universities before the start of the fall semester to reinforce hygiene practices. Additional efforts to address multidrug-resistant Shigella and to support prevention in high-risk groups such as unhoused individuals and men who have sex with men (MSM) remain essential.

Acknowledgments

We would like to thank the Centers for Disease Control and Prevention’s National Outbreak Reporting System Team for their invaluable support in providing the Shigella outbreak data used in this study. We also thank the National Institutes of Health (NIH) for providing the Joinpoint software, which is essential for trend analysis. All analyses and the writing of this manuscript were performed solely by the authors.

Funding Statement

The author(s) declared that financial support was not received for this work and/or its publication.

Footnotes

Edited by: Zhongjie Shi, Wayne State University, United States

Reviewed by: Eustachio Cuscianna, University of Bari Aldo Moro, Italy

Gloria Aguilar, Instituto Nacional de Salud, Paraguay

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html CDC NORS.

Ethics statement

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

Author contributions

MB: Conceptualization, Methodology, Writing – review & editing, Data curation, Formal analysis, Validation, Visualization, Writing – original draft. CA: Validation, Writing – review & editing. RS: Writing – review & editing, Conceptualization, Investigation, Methodology, Project administration.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1740303/full#supplementary-material

Table_1.DOCX (1.7MB, DOCX)

References

  • 1.Baharvand A, Molaeipour L, Alesaeidi S, Shaddel R, Mashatan N, Amiriani T, et al. The increasing antimicrobial resistance of Shigella species among Iranian pediatrics: a systematic review and meta-analysis. Pathog Glob Health. (2023) 117:611–22. doi: 10.1080/20477724.2023.2179451, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Hines JZ, Pinsent T, Rees K, Vines J, Bowen A, Hurd J, et al. Notes from the field: shigellosis outbreak among men who have sex with men and homeless persons — Oregon, 2015–2016. MMWR Morb Mortal Wkly Rep. (2016) 65:812. doi: 10.15585/mmwr.mm6531a5, [DOI] [PubMed] [Google Scholar]
  • 3.Zaidi MB, Estrada-García T. Shigella: a highly virulent and elusive pathogen. Curr Trop Med Rep. (2014) 1:81–7. doi: 10.1007/s40475-014-0019-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.CDC Shigella prevention and control toolkit—March 2021. Atlanta, GA: Centers for Disease Control and Prevention. (2020). [Google Scholar]
  • 5.Faherty EAG, Kling K, Barbian HJ, Qi C, Altman S, Dhiman VK, et al. Extended-Spectrum β-lactamase Shigella sonnei cluster among men who have sex with men in Chicago, Illinois, July–October 2022. J Infect Dis. (2025) 231:692–5. doi: 10.1093/infdis/jiae235, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wikswo ME, Roberts V, Marsh Z, Manikonda K, Gleason B, Kambhampati A, et al. Enteric illness outbreaks reported through the National Outbreak Reporting System - United States, 2009-2019. Clin Infect Dis. (2022) 74:1906–13. doi: 10.1093/cid/ciab771, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Mattison CP, Calderwood LE, Marsh ZA, Wikswo ME, Balachandran N, Kambhampati AK, et al. Childcare and school acute gastroenteritis outbreaks: 2009-2020. Pediatrics. (2022) 150:e2021056002. doi: 10.1542/peds.2021-056002, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hines JZ, Jagger MA, Jeanne TL, West N, Winquist A, Robinson BF, et al. Heavy precipitation as a risk factor for shigellosis among homeless persons during an outbreak — Oregon, 2015–2016. J Infect. (2018) 76:280–5. doi: 10.1016/j.jinf.2017.11.010, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gharpure R, Marsh ZA, Tack DM, Collier SA, Strysko J, Ray L, et al. Disparities in incidence and severity of Shigella infections among children-foodborne diseases active surveillance network (FoodNet), 2009-2018. J Pediatric Infect Dis Soc. (2021) 10:782–8. doi: 10.1093/jpids/piab045, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Badr HS, Colston JM, Nguyen NLH, Chen YT, Burnett E, et al. Spatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant data. Lancet Glob Health (2023) 11:e373–e384. doi: 10.1016/S2214-109X(22)00549-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gao L, Zhang Y, Ding G, Liu Q, Zhou M, Li X, et al. Meteorological variables and bacillary dysentery cases in Changsha City, China. Am J Trop Med Hyg. (2014) 90:697–704. doi: 10.4269/ajtmh.13-0198, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zhao Z, Yang M, Lv J, Hu Q, Chen Q, Lei Z, et al. Shigellosis seasonality and transmission characteristics in different areas of China: a modelling study. Infect Dis Model. (2022) 7:161–78. doi: 10.1016/j.idm.2022.05.003, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ray LC, Griffin PM, Wymore K, Wilson E, Hurd S, Laclair B, et al. Changing diagnostic testing practices for foodborne pathogens, foodborne diseases active surveillance network, 2012-2019. Open Forum Infect Dis. (2022) 9:ofac344. doi: 10.1093/ofid/ofac344 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tack DM Ray L Griffin PM Cieslak PR Dunn J Rissman T et al.. Preliminary incidence and trends of infections with pathogens transmitted commonly through food — Foodborne diseases active surveillance network, 10 U.S. sites, 2016–2019. MMWR. Morbidity and mortality weekly report 69 (2020). 2016–2019 [DOI] [PMC free article] [PubMed]
  • 15.Marder EP, Griffin PM, Cieslak PR, Dunn J, Hurd S, Jervis R, et al. Incidence and trends of infections with pathogens transmitted commonly through food and the effect of increasing use of culture-independent diagnostic tests on surveillance—Foodborne Diseases Active Surveillance Network, 10 US sites, 2013–2016. MMWR. Morbidity and mortality weekly report, 66, (2017). [DOI] [PMC free article] [PubMed]
  • 16.Tack DM, Marder EP, Griffin PM, Cieslak PR, Dunn J, Hurd S, et al. Preliminary Incidence and Trends of Infections with Pathogens Transmitted Commonly through Food—Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2015–2018. MMWR. Morbidity and Mortality Weekly Report. (2019) 68:369–73. doi: 10.15585/mmwr.mm6816a2, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ray LC, Collins JP, Griffin PM, Shah HJ, Boyle MM, Cieslak PR, et al. Decreased Incidence of Infections Caused by Pathogens Transmitted Commonly Through Food During the COVID-19 Pandemic — Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2017–2020. MMWR Morb Mortal Wkly Rep (2021) 70:1332–36. doi: 10.15585/mmwr.mm7038a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Kunz JM, Lawinger H, Miko S, Gerdes M, Thuneibat M, Hannapel E, et al. Surveillance of waterborne disease outbreaks associated with drinking water — United States, 2015–2020. MMWR Surveill Summ. (2024) 73:1–23. doi: 10.15585/mmwr.ss7301a1, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Vanden Esschert KL, Mattioli MC, Hilborn ED, Roberts VA, Yu AT, Lamba K, et al. Outbreaks associated with untreated recreational water — California, Maine, and Minnesota, 2018–2019. MMWR Morb Mortal Wkly Rep. (2020) 69:781. doi: 10.15585/mmwr.mm6925a3, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wikswo ME, Kambhampati A, Shioda K, Walsh KA, Bowen A, Hall AJ. Outbreaks of acute gastroenteritis transmitted by person-to-person contact, environmental contamination, and unknown modes of transmission--United States, 2009-2013. MMWR Surveill Summ. (2015) 64:1. doi: 10.15585/mmwr.mm6412a1, [DOI] [PubMed] [Google Scholar]
  • 21.Wikswo ME, Hall AJ. Centers for Disease Control. Outbreaks of acute gastroenteritis transmitted by person-to-person contact--United States, 2009-2010. MMWR Surveill Summ. (2012) 61:1–12. [PubMed] [Google Scholar]
  • 22.Kim HJ, Chen HS, Byrne J, Wheeler B, Feuer EJ. Twenty years since Joinpoint 1.0: two major enhancements, their justification, and impact. Stat Med. (2022) 41:3102–30. doi: 10.1002/sim.9407, [DOI] [PubMed] [Google Scholar]
  • 23.National Center for Immunization and Respiratory Diseases (U.S.) Division of viral diseases National Outbreak Reporting System (NORS) guidance: Version 3.0, updated 1/23/2023. Atlanta, GA: Centers for Disease Control and Prevention. (2023). [Google Scholar]
  • 24.US Census Bureau. United States and state population estimates from 2009 to 2022. Washington, DC: United States Census Bureau. (2025). Available online at: https://www.census.gov/en.html [Accessed May 5, 2025] [Google Scholar]
  • 25.R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. (2025). [Google Scholar]
  • 26.Posit team RStudio: Integrated development environment for R. Bethesda, MD: National Institutes of Health. (2025) [Google Scholar]
  • 27.Surveillance Research Program NCI. Joinpoint regression software - version 5.4.0 - April 2025. (2025). Available online at: https://surveillance.cancer.gov/joinpoint [Accessed October 27, 2025]
  • 28.Zhang NR, Siegmund DO. A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics. (2007) 63:22–32. doi: 10.1111/j.1541-0420.2006.00662.x, [DOI] [PubMed] [Google Scholar]
  • 29.Sivapalasingam S, Nelson JM, Joyce K, Hoekstra M, Angulo FJ, Mintz ED. High prevalence of antimicrobial resistance among Shigella isolates in the United States tested by the national antimicrobial resistance monitoring system from 1999 to 2002. Antimicrob Agents Chemother. (2006) 50:49–54. doi: 10.1128/AAC.50.1.49-54.2006, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.European Centre for Disease Prevention and Control. Shigellosis. In. ECDC. Annual Epidemiological Report for 2022. Stockholm: ECDC. (2024). Available online at: https://www.ecdc.europa.eu/en/publications-data/shigellosis-annual-epidemiological-report-2022 [Google Scholar]
  • 31.Khalil IA, Troeger C, Blacker BF, Rao PC, Brown A, Atherly DE, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: the global burden of disease study 1990–2016. Lancet Infect Dis. (2018) 18:1229–40. doi: 10.1016/S1473-3099(18)30475-4, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kyu HH, Vongpradith A, Dominguez RMV, Ma J, Albertson SB, Novotney A, et al. Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990–2021, for 204 countries and territories: a systematic analysis for the global burden of disease study 2021. Lancet Infect Dis. (2025) 25:519–36. doi: 10.1016/S1473-3099(24)00691-1, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Huang JY, Henao OL, Griffin PM, Vugia DJ, Cronquist AB, Hurd S, et al. Infection with pathogens transmitted commonly through food and the effect of increasing use of culture-independent diagnostic tests on surveillance — foodborne diseases active surveillance network, 10 U.S. sites, 2012–2015. MMWR Morb Mortal Wkly Rep. (2016) 65:368–71. doi: 10.15585/mmwr.mm6514a2 [DOI] [PubMed] [Google Scholar]
  • 34.Baker S, The HC. Recent insights into Shigella: a major contributor to the global diarrhoeal disease burden. Curr Opin Infect Dis. (2018) 31:449–54. doi: 10.1097/QCO.0000000000000475, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Eikmeier D, Talley P, Bowen A, Leano F, Dobbins G, Jawahir S, et al. Decreased susceptibility to azithromycin in clinical Shigella isolates associated with HIV and sexually transmitted bacterial diseases, Minnesota, USA, 2012–2015. Emerg Infect Dis. (2020) 26:667–74. doi: 10.3201/eid2604.191031, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.June M/, Bowen A, Eikmeier D, Talley P, Siston A, Smith S, et al. Morbidity and mortality weekly report notes from the field outbreaks of Shigella sonnei infection with decreased susceptibility to azithromycin among men who have sex with men-Chicago and metropolitan Minneapolis-St. Paul, 2014 (2015). 597 p. [PMC free article] [PubMed]
  • 37.Mason LCE, Greig DR, Cowley LA, Partridge SR, Martinez E, Blackwell GA, et al. The evolution and international spread of extensively drug resistant Shigella sonnei. Nat Commun. (2023) 14. doi: 10.1038/s41467-023-37672-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bassal R, Keinan-Boker L, Cohen D. A significant decrease in the incidence of shigellosis in Israel during covid-19 pandemic. Int J Environ Res Public Health. (2021) 18:1–7. doi: 10.3390/ijerph18063070, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Delahoy MJ, Shah HJ, Weller DL, Ray LC, Smith K, McGuire S, et al. Preliminary incidence and trends of infections caused by pathogens transmitted commonly through food — foodborne diseases active surveillance network, 10 U.S. sites, 2022. MMWR Morb Mortal Wkly Rep. (2023) 72. doi: 10.15585/mmwr.mm7226a1, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Collins JP, Shah HJ, Weller DL, Ray LC, Smith K, McGuire S, et al. Preliminary incidence and trends of infections caused by pathogens transmitted commonly through food — foodborne diseases active surveillance network, 10 U.S. sites, 2016–2021. MMWR Morb Mortal Wkly Rep. (2022) 71:1260–64. doi: 10.15585/mmwr.mm7140a2, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Cai M, Xu E, Xie Y, Al-Aly Z. Rates of infection with other pathogens after a positive COVID-19 test versus a negative test in US veterans (November, 2021, to December, 2023): a retrospective cohort study. Lancet Infect Dis. (2025) 25:847–60. doi: 10.1016/S1473-3099(24)00831-4, [DOI] [PubMed] [Google Scholar]
  • 42.Ding S, Zhang T, Lei Y, Liu C, Liu Z, Fu R. The role of TIM3+NK and TIM3-NK cells in the immune pathogenesis of severe aplastic anemia. J Transl Intern Med. (2024) 12:96–105. doi: 10.2478/jtim-2023-0104, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Guo Z, Liu Y, Chen D, Sun Y, Li D, Meng Y, et al. Targeting regulated cell death: apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis in anticancer immunity. J Transl Int Med. (2025) 13:10–32. doi: 10.1515/jtim-2025-0004, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Oda G, Chung J, Lucero-Obusan C, Holodniy M. Bacteremic vs nonbacteremic Shigella infections in the United States veterans health administration, 2000–2024. Open Forum Infect Dis. (2025) 12:ofaf488. doi: 10.1093/ofid/ofaf488, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Stefanovic A, Matic N, Ritchie G, Lowe CF, Leung V, Hull M, et al. Multidrug-resistant Shigella sonnei bacteremia among persons experiencing homelessness, Vancouver, British Columbia, Canada. Emerg Infect Dis. (2023) 29:1668–71. doi: 10.3201/eid2908.230323, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Murray K, Reddy V, Kornblum JS, Waechter H, Chicaiza LF, Rubinstein I, et al. Increasing antibiotic resistance in Shigella spp. from infected New York city residents, New York, USA. Emerg Infect Dis. (2017) 23:332–5. doi: 10.3201/eid2302.161203, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Ewing AG, Salamon S, Pretorius E, Joffe D, Fox G, Bilodeau S, et al. Review of organ damage from COVID and long COVID: a disease with a spectrum of pathology. Medical Review. (2025) 5:66–75. doi: 10.1515/mr-2024-0030, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Guo M, Shang S, Li M, Cai G, Li P, Chen X, et al. Understanding autoimmune response after SARS-CoV-2 infection and the pathogenesis/mechanisms of long COVID. Medical Review. (2024) 4:367–83. doi: 10.1515/mr-2024-0013, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Lindsay B, Oundo J, Hossain MA, Antonio M, Tamboura B, Walker AW, et al. Microbiota that affect risk for shigellosis in children in low-income countries. Emerg Infect Disease. (2015) 21:242–50. doi: 10.3201/eid2102.140795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Hegde S, Benoit SR, Arvelo W, Lindblade K, López B, McCracken JP, et al. Burden of laboratory-confirmed shigellosis infections in Guatemala 2007-2012: results from a population-based surveillance system. BMC Public Health. (2019) 19:474. doi: 10.1186/s12889-019-6780-7, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Lee HS, Ha Hoang TT, Pham-Duc P, Lee M, Grace D, Phung DC, et al. Seasonal and geographical distribution of bacillary dysentery (shigellosis) and associated climate risk factors in Kon Tam Province in Vietnam from 1999 to 2013. Infect Dis Poverty. (2017) 6:113. doi: 10.1186/s40249-017-0325-z, [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Arvelo W, Hinkle CJ, Nguyen TA, Weiser T, Steinmuller N, Khan F, et al. Transmission risk factors and treatment of pediatric shigellosis during a large daycare center-associated outbreak of multidrug resistant shigella sonnei: implications for the management of shigellosis outbreaks among children. Pediatr Infect Dis J. (2009) 28:976–80. doi: 10.1097/INF.0b013e3181a76eab, [DOI] [PubMed] [Google Scholar]
  • 53.Bianchi FP, Stefanizzi P, Rizzi D, Signorile N, Cuscianna E, Daleno A, et al. Burden of COVID-19 disease and vaccine coverages in Apulian splenectomized patients: a retrospective observational study. Br J Haematol. (2023) 201:1072–80. doi: 10.1111/bjh.18731, [DOI] [PubMed] [Google Scholar]
  • 54.Bianchi FP, Stefanizzi P, Di Lorenzo A, Cuscianna E, Tafuri S, Germinario CA. Vaccine coverage for recommended vaccines among splenectomised patients in Apulia, South Italy: a retrospective cohort study. BMJ Open. (2023) 13:e069316. doi: 10.1136/bmjopen-2022-069316, [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table_1.DOCX (1.7MB, DOCX)

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/ncezid/dfwed/BEAM-dashboard.html CDC NORS.

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